EAGER-QAC-QCH: Hybrid Quantum Classical Algortithm for NMR Inference

哈佛大学的Eugene Demler获得了化学系化学理论、模型和计算方法项目的EAGER奖，研究核磁共振推断的混合量子-经典算法。材料研究部的凝聚态物质和材料项目也共同资助了该奖项。该提案是为了响应量子算法挑战Dear Colleague Letter， NSF 20-056而提交的。核磁共振是医学和生物学中最强大的分析技术之一。它适用于体内和体外研究。然而，在核磁共振实验中，对数据的解释是困难的。化合物的光谱分析是一个复杂的模式识别问题。核磁共振化合物鉴定的光谱分析和解释繁琐而缓慢。它可能在不同平台上不一致。通常难以将其扩展为新的化合物，这是药物发现和作用机制鉴定的重要障碍。因此，化合物鉴定是在医学、工程和科学领域实现这些技术的主要挑战。量子相关和纠缠，而不是传统的相关定义核磁共振波谱。因此，解决光谱推理问题的量子方法非常适合实现备受追捧的量子优势。Eugene Demler正在开发混合方法，将数据科学工具、深度学习方法与量子计算相结合，以解决光谱分析推理问题。这项工作可以在医学、科学和工程的许多应用中产生广泛的影响，在代谢组学化合物鉴定中有直接的应用，它侧重于分析细胞、器官和体液内的小分子。核磁共振是医学和生物学中最强大的分析技术之一，因为它适用于体内和体外研究。然而，在核磁共振实验中，对数据的解释是困难的。人们只直接观察生物样本的磁谱，而最终的目标是了解潜在的微观哈密顿量，并最终识别和量化化合物。Eugene Demler正在开发一种结合量子计算、量子模拟和经典机器学习的混合方法，以解决核磁共振推断的问题。这种方法的新颖之处在于使用量子模拟器计算哈密顿量的假设值的谱，然后使用经典的深度学习算法来优化这些参数。Eugene Demler的工作解决了以下具体问题：(i)在当前可用的量子计算平台上寻找采样NMR谱的最佳协议，重点是离子链和Rydberg阵列；（ii）通过寻找更有效的哈密顿演化的trotter化方法来改进混合算法；（iii）利用变分贝叶斯高斯方法的LASSO正则化改进经典优化过程；（iv）建立量子辅助核磁共振推断的理论界限；(v)与实验组合作，提供量子辅助核磁共振推断的原理证明实验演示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Eugene Demler of Harvard University is supported by an EAGER award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to study Hybrid Quantum-Classical Algorithms for NMR Inference. The Condensed Matter and Materials program in the Division of Materials Research also cofunds this award. The proposal was submitted in response to the Quantum Algorithm Challenge Dear Colleague Letter, NSF 20-056. NMR is one of the most powerful analytical techniques available to medicine and biology. It is suited for both in vivo and in vitro studies. Yet it is difficult to interpret the data in NMR experiments. Spectral profiling of compounds is a complex pattern recognition problem. Spectroscopic analysis and interpretation for NMR compound identification is cumbersome and slow. It can be inconsistent across platforms. It is generally hard to scale it to novel compounds, an important obstacle to drug discovery and identification of mechanism of action. Therefore, compound identification is a major challenge for implementing these technologies in medicine, engineering, and science. Quantum correlations and entanglement rather than traditional correlations define NMR spectra. Therefore, quantum approaches to solve spectroscopic inference problems are well-suited to achieve a sought-after quantum advantage. Eugene Demler is developing hybrid approaches that combine the tools of data science, deep learning methods, with quantum computing to address the problem of inference of spectral analysis. This work can have a wide impact in many applications of NMR in medicine, science and engineering, with an immediate application in metabolomics compound identification, which focuses on profiling small molecules inside cells, organs and body fluids. NMR is one of the most powerful analytical techniques available to medicine and biology, as it is suited for both in vivo and in vitro studies. Yet it is difficult to interpret the data in NMR experiments. One directly observes only the magnetic spectrum of a biological sample, whereas the ultimate goal is to learn about the underlying microscopic Hamiltonian and ultimately identify and quantify chemical compounds. Eugene Demler is developing a hybrid approach that combines quantum computing, quantum simulations, and classical machine learning to address the problem of NMR inference. The novelty of this approach is in using quantum simulators to compute spectra for hypothetical values of the Hamiltonians, and then using classical deep-learning algorithms to optimize these parameters. Eugene Demler’s work address the following specific questions: (i) Finding optimal protocols for sampling NMR spectra on currently available quantum computing platforms with a focus on ion chains and Rydberg arrays; (ii) improving hybrid algorithms through finding more efficient methods of Trotterizing the Hamiltonian evolution; (iii) improving the classical optimization procedure using LASSO regularization of the variational Bayesian Gaussian method; (iv) establishing the theoretical limits to quantum assisted NMR inference ; (v) collaborating with experimental groups to provide a proof of principle experimental demonstration of quantum assisted NMR inference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.